import pandas as pd
import numpy as np
import os
import datetime
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn import tree
import pytz
import itertools
import visualize
import utils
import pydotplus
import xgboost as xgb
from sklearn import metrics
import pvlib
import cs_detection
import visualize_plotly as visualize
from IPython.display import Image
%load_ext autoreload
%autoreload 2
np.set_printoptions(precision=4)
%matplotlib inline
Only making ground predictions using PVLib clearsky model and statistical model. NSRDB model won't be available to ground measurements.
nsrdb = cs_detection.ClearskyDetection.read_pickle('srrl_nsrdb_1.pkl.gz', 'GHI', 'Clearsky GHI pvlib', 'sky_status')
nsrdb.df.index = nsrdb.df.index.tz_convert('MST')
ground = cs_detection.ClearskyDetection.read_pickle('srrl_ground_1.pkl.gz', 'GHI', 'Clearsky GHI pvlib')
ground.df.index = ground.df.index.tz_convert('MST')
ground.df.index[0], ground.df.index[-1]
nsrdb.df.index[0], nsrdb.df.index[-1]
ground2 = cs_detection.ClearskyDetection(ground.df, 'GHI', 'Clearsky GHI pvlib', solar_noon_col='abs(t-tnoon)')
ground2.trim_dates('01-01-2002', '01-01-2015')
ground2.df = ground2.df[ground2.df.index.minute % 30 == 0]
nsrdb2 = cs_detection.ClearskyDetection(nsrdb.df, 'GHI', 'Clearsky GHI pvlib', 'sky_status', solar_noon_col='abs(t-tnoon)')
nsrdb2.trim_dates('01-01-2002', '01-01-2015')
nsrdb2.df = nsrdb2.df[nsrdb2.df.index.minute % 30 == 0]
vis = visualize.Visualizer()
vis.add_line_ser(ground2.df['GHI'], 'Grnd GHI')
vis.add_line_ser(ground2.df['Clearsky GHI pvlib'], 'Grnd GHIcs')
vis.add_line_ser(nsrdb2.df['GHI'], 'NSRDB GHI')
vis.add_line_ser(nsrdb2.df['Clearsky GHI pvlib'], 'NSRDB GHIcs')
vis.show()